Image segmentation is the problem of extracting or “segmenting” objects of interest from non-interesting background information in an image. Reliable image segmentation algorithms are required in many fields, particularly for medical images such as CT (computerized tomography) scans, MRI (magnetic resonance imaging) scans, PET (positron emission tomography) scans and the like. For the field of medical image processing, it is important to be able to accurately, rapidly and reliably perform segmentation, as medical diagnoses increasingly rely upon such information. As a non-limiting example, detection and determination of various physical parameters of lesions, such as volume, longest diameter and so forth, are important for the diagnosis of many diseases. In addition, determination of the growth rate of such lesions is also important for disease diagnosis and prognosis.
Fully automatic image segmentation has so far proven to be impractical and unreliable, particularly for three dimensional images such as those which arise from the above described scans. These three dimensional images are provided as a series of two dimensional slices; image segmentation algorithms must therefore relate to both individual two dimensional slices and to the overall three dimensional construct that is composed of a plurality of such slices. Therefore, many currently available medical image processing algorithms rely upon a mixture of automated and manual algorithms, which require human interaction with the medical image data.
Certain algorithms are available for performing such segmentation which only require minimal user input, including for example those algorithms taught in U.S. Pat. No. 7,773,791 to Simon et al, filed on Dec. 7, 2006, issued on Aug. 10, 2010; U.S. Pat. No. 7,844,087 to Ray et al, filed on Dec. 19, 2006, issued on Nov. 30, 2010; US Patent Application No. 20090279754 to Gindele et al, filed on Dec. 22, 2008, published on Nov. 12, 2009; and U.S. patent application Ser. No. 12/823,346 to Simon, filed on Jun. 25, 2010; all of which are, hereby incorporated by reference as if fully set forth herein. These algorithms require only minimal inputs, such as a single point in the lesion or a line corresponding to a long diameter, which are typically entered by the user, for example by indicating or “clicking on” a point on and/or in an image of a lesion with a mouse or other pointing device through a computer display of such an image. Given such inputs these algorithms compute a 3D (three dimensional) segmentation of the lesion as well as a longest diameter and a second diameter. These diameters form part of the diagnostic standard of care as determined for example by the RECIST (response evaluation criteria in solid tumors) and WHO (World Health Organization) guidelines. However, as such lesion measurements are inherently inaccurate, given that they rely upon only linear measurements, optionally and preferably lesion volume is calculated as described herein through segmentation, which is more accurate. The determination of lesion volume over time is also an important diagnostic tool, which is more accurately performed through comparisons between segmented lesions in 3D image data at a plurality of time points.
Currently available tools, such as the PACS system from Carestream Healthcare Inc (USA); as presented during the RSNA meeting of November 2010, display the segmented lesion contours within a user display, along with the volume and diameter measurements and the lines representing the diameters. In order not to hide the data, the diameter lines and/or the lesion contours may optionally appear only when the user hovers over a given lesion. The system also automatically adds these measurements to the radiology report. It further allows grouping of lesions across different studies (image data acquired on different dates) of the same patient, and automatically computes growth rate and doubling time for such grouped lesions in the report. However, the system currently requires the lesions to be determined at least partially according to user input as described above in all the relevant studies before such grouping may be performed.